Conversational AI Applications in Ed‐Tech Industry

Chapter 25 Conversational AI Applications in Ed-Tech Industry An Analysis of Its Impact and Potential in Education Deepika Chauhan, Deepika Chauhan Computer Application Department, Silver Oak University, Ahmedabad, Gujarata, IndiaSearch for more papers by this authorChaitanya Singh, Chaitanya Singh Computer Engineering Department, Vidhyadeep University, Surat, Gujarata, IndiaSearch for more papers by this authorRomil Rawat, Romil Rawat Department of Computer Science, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, IndiaSearch for more papers by this authorMukesh Chouhan, Mukesh Chouhan HOD (Computer Science & Engineering), Government Polytechnic College, Khargone, Sanawad, IndiaSearch for more papers by this author Deepika Chauhan, Deepika Chauhan Computer Application Department, Silver Oak University, Ahmedabad, Gujarata, IndiaSearch for more papers by this authorChaitanya Singh, Chaitanya Singh Computer Engineering Department, Vidhyadeep University, Surat, Gujarata, IndiaSearch for more papers by this authorRomil Rawat, Romil Rawat Department of Computer Science, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, IndiaSearch for more papers by this authorMukesh Chouhan, Mukesh Chouhan HOD (Computer Science & Engineering), Government Polytechnic College, Khargone, Sanawad, IndiaSearch for more papers by this author Book Editor(s):Romil Rawat, Romil RawatSearch for more papers by this authorRajesh Kumar Chakrawarti, Rajesh Kumar ChakrawartiSearch for more papers by this authorSanjaya Kumar Sarangi, Sanjaya Kumar SarangiSearch for more papers by this authorPiyush Vyas, Piyush VyasSearch for more papers by this authorMary Sowjanya Alamanda, Mary Sowjanya AlamandaSearch for more papers by this authorKotagiri Srividya, Kotagiri SrividyaSearch for more papers by this authorKrishnan Sakthidasan Sankaran, Krishnan Sakthidasan SankaranSearch for more papers by this author First published: 27 January 2024 https://doi.org/10.1002/9781394200801.ch25 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary Conversational artificial intelligence (AI) has the potential to revolutionize the field of education. Its ability to understand natural language and engage in human-like conversations makes it a valuable tool for personalized learning and student engagement. This paper discusses the current state of conversational AI in education, including its use in tutoring systems, virtual learning assistants, and language learning applications. The research studies the effectiveness of conversational AI in improving student learning outcomes and engagement and highlights the potential challenges and limitations of this technology. Through this paper, we aim to provide an overview of the current research on the use of conversational AI in education and to offer insights into how this technology can be used to enhance student learning and engagement in the future. References 101 Artificial intelligence statistics [updated for 2022], Techjury, 2022 , November 26, https://techjury.net/blog/ai-statistics/ . Google Scholar Wadhwani , P. and Loomba , S. , Artificial Intelligence (AI) in education market size by component (solution, service [professional service, managed service]), by deployment (on-premise, cloud), by technology (machine learning, deep earning, Natural Language Processing (NLP)), by application (learning platform & virtual facilitators, Intelligent Tutoring System (ITS), smart content, fraud & risk management), by end-use (higher education, K-12 education, corporate learning), COVID-19 impact analysis, regional outlook, growth potential, competitive market share & forecast, 2022 - 2030 , Global Market Insights Inc. , 2022 , June 27, https://www.gmin-sights.com/industry-analysis/artificial-intelligence-ai-in-education-market . Google Scholar 2023 , Retrieved 6 January 2023, from https://yellow.ai/education-chatbot/ . Google Scholar Conversational AI, What is conversational AI?, Interactions , 2023 , Retrieved 6 January 2023, from https://www.interactions.com/conversational-ai/ . Google Scholar Kitchenham , B. and Charters , S. , Guidelines for performing systematic literature reviews in software engineering , 2007 . Google Scholar Chatterjee , J. and Dethlefs , N. , This new conversational AI model can be your friend, philosopher, and guide … and even your worst enemy . Patterns , 4 , 1 , 100676 , 2023 . 10.1016/j.patter.2022.100676 Google Scholar Zhang , D. , Affective cognition of students' autonomous learning in College English teaching based on deep learning . Front. Psychol. , 12 , 6601 , 2022 . 10.3389/fpsyg.2021.808434 Web of Science®Google Scholar Zhai , C. , Wibowo , S. , Cowling , M. , Work-in-progress—Embedding cross-cultural humorous and empathetic functions to facilitate language acquisition , in: 2022 8th International Conference of the Immersive Learning Research Network (iLRN) , 2022 , May, IEEE , pp. 1 – 4 . Google Scholar Xie , Y. , Svikhnushina , E. , Pu , P. , A multi-turn emotionally engaging dialog model . arXiv preprint arXiv:1908.07816 ., 2019 . Google Scholar Wu , C.H. , Lin , H.C.K. , Wang , T.H. , Huang , T.H. , Huang , Y.M. , Affective mobile language tutoring system for supporting language learning . Front. Psychol. , 13 , 833327 , 2022 . 10.3389/fpsyg.2022.833327 PubMedWeb of Science®Google Scholar Xie , Y. , Liu , Y. , Zhang , F. , Zhou , P. , Virtual reality-integrated immersion-based teaching to English language learning outcome . Front. Psychol. , 12 , 767363 , 2022 . 10.3389/fpsyg.2021.767363 PubMedWeb of Science®Google Scholar Wang , Y. , Grant , S. , Grist , M. , Enhancing the learning of multi-level undergraduate Chinese language with a 3D immersive experience-an exploratory study . Comput. Assisted Lang. Learn. , 34 , 1-2 , 114 – 132 , 2021 . 10.1080/09588221.2020.1774614 Web of Science®Google Scholar Weng , C. , Otanga , S. , Weng , A. , Cox , J. , Effects of interactivity in E-textbooks on 7th graders science learning and cognitive load . Comput. Educ. , 120 , 172 – 184 , 2018 . 10.1016/j.compedu.2018.02.008 Web of Science®Google Scholar Wu , T.T. and Chen , A.C. , Combining e-books with mind mapping in a reciprocal teaching strategy for a classical Chinese course . Comput. Educ. , 116 , 64 – 80 , 2018 . 10.1016/j.compedu.2017.08.012 Web of Science®Google Scholar Wang , Y. , Grant , S. , Grist , M. , Enhancing the learning of multi-level undergraduate Chinese language with a 3D immersive experience-an exploratory study . Comput. Assisted Lang. Learn. , 34 , 1-2 , 114 – 132 , 2021 . 10.1080/09588221.2020.1774614 Web of Science®Google Scholar Halabi , O. , Immersive virtual reality to enforce teaching in engineering education . Multimedia Tools Appl. , 79 , 3-4 , 2987 – 3004 , 2020 . 10.1007/s11042-019-08214-8 Web of Science®Google Scholar Seo , J.H. , Bruner , M. , Payne , A. , Gober , N. , McMullen , D. , Chakravorty , D.K. , Using virtual reality to enforce principles of cybersecurity . J. Comput. Sci. Educ. , 10 , 1 , 2019 . 10.22369/issn.2153-4136/10/1/13 Google Scholar Sochacka , N.W. , Guyotte , K.W. , Walther , J. , Learning together: A collaborative autoethno-graphic exploration of STEAM (STEM+ the Arts) education . J. Eng. Educ. , 105 , 1 , 15 – 42 , 2016 . 10.1002/jee.20112 Web of Science®Google Scholar Cope , B. , Kalantzis , M. , Searsmith , D. , Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies . Educ. Philos. Theory , 53 , 12 , 1229 – 1245 , 2021 . 10.1080/00131857.2020.1728732 Web of Science®Google Scholar Martínez , F. , Herrero , L.C. , De Pablo , S. , Project-based learning and rubrics in the teaching of power supplies and photovoltaic electricity . IEEE Trans. Educ. , 54 , 1 , 87 – 96 , 2010 . 10.1109/TE.2010.2044506 Web of Science®Google Scholar Pogorskiy , E. and Beckmann , J.F. , From procrastination to engagement? An experimental exploration of the effects of an adaptive virtual assistant on self-regulation in online learning . Comput. Educ.: Artif. Intell. , 4 , 100111 , 2023 . 10.1016/j.caeai.2022.100111 Google Scholar Menictas , M. , Rabbi , M. , Klasnja , P. , Murphy , S. , Artificial intelligence decision-making in mobile health . Biochemist , 41 , 5 , 20 – 24 , 2019 . 10.1042/BIO04105020 Google Scholar Blodgett , N.P. , Howard , V.M. , Phillips , B.C. , Andolsek , K. , Richard-Eaglin , A. , Molloy , M.A. , Developing virtual simulations to confront racism and bias in health professions education . Clin. Simul. Nurs. , 71 , 105 – 111 , 2022 . 10.1016/j.ecns.2022.03.009 Web of Science®Google Scholar Zhu , Y. , Nie , J.Y. , Zhou , K. , Du , P. , Dou , Z. , Content selection network for document-grounded retrieval-based chatbots , in: Advances in Information Retrieval: 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28–April 1, 2021, Proceedings, Part I 43 , Springer International Publishing , pp. 755 – 769 , 2021 . 10.1007/978-3-030-72113-8_50 Google Scholar Mahor , V. , Bijrothiya , S. , Rawat , R. , Kumar , A. , Garg , B. , Pachlasiya , K. , IoT and artificial intelligence techniques for public safety and security , in: Smart Urban Computing Applications , p. 111 , 2023 . Google Scholar Mahor , V. , Pachlasiya , K. , Garg , B. , Chouhan , M. , Telang , S. , Rawat , R. , Mobile operating system (Android) vulnerability analysis using machine learning , in: Proceedings of International Conference on Network Security and Blockchain Technology: ICNSBT 2021 , pp. 159 – 169 , Springer Nature Singapore , Singapore , 2022 , June. 10.1007/978-981-19-3182-6_13 Google Scholar Rawat , R. , Garg , B. , Pachlasiya , K. , Mahor , V. , Telang , S. , Chouhan , M. , Mishra , R. , SCNTA: Monitoring of network availability and activity for identification of anomalies using machine learning approaches . Int. J. Inf. Technol. Web Eng. (IJITWE) , 17 , 1 , 1 – 19 , 2022 . 10.4018/IJITWE.304051 Web of Science®Google Scholar Rawat , R. , Rimal , Y.N. , William , P. , Dahima , S. , Gupta , S. , Sankaran , K.S. , Malware threat affecting financial organization analysis using machine learning approach . Int. J. Inf. Technol. Web Eng. (IJITWE) , 17 , 1 , 1 – 20 , 2022 . 10.4018/IJITWE.304051 Web of Science®Google Scholar Rawat , R. , Mahor , V. , Chouhan , M. , Pachlasiya , K. , Telang , S. , Garg , B. , Systematic literature review (SLR) on social media and the digital transformation of drug trafficking on darkweb , in: International Conference on Network Security and Blockchain Technology , pp. 181 – 205 , Springer , Singapore , 2022 . 10.1007/978-981-19-3182-6_15 Google Scholar Rawat , R. , Ayodele Oki , O. , Sankaran , S. , Florez , H. , Ajagbe , S.A. , Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime . Int. J. Electr. Comput. Eng. (IJECE) , 13 , 5 , 5354 – 5365 , Oct. 2023 , doi: 10.11591/ijece.v13i5.pp5354-5365 . 10.11591/ijece.v13i5.pp5354-5365 Google Scholar Rawat , R. , Garg , B. , Mahor , V. , Telang , S. , Pachlasiya , K. , Chouhan , M. , Organ trafficking on the dark web—The data security and privacy concern in healthcare systems , in: Internet of Healthcare Things: Machine Learning for Security and Privacy , pp. 189 – 216 , 2022 . 10.1002/9781119792468.ch9 Google Scholar Vyas , P. , Vyas , G. , Chauhan , A. , Rawat , R. , Telang , S. , Gottumukkala , M. , Anonymous trading on the dark online marketplace: An exploratory study , in: Using Computational Intelligence for the Dark Web and Illicit Behavior Detection , pp. 272 – 289 , IGI Global , 2022 . 10.4018/978-1-6684-6444-1.ch015 Google Scholar Rawat , R. , Oki , O.A. , Sankaran , K.S. , Olasupo , O. , Ebong , G.N. , Ajagbe , S.A. , A new solution for cyber security in big data using machine learning approach , in: Mobile Computing and Sustainable Informatics: Proceedings of ICMCSI 2023 , pp. 495 – 505 , Springer Nature Singapore , Singapore , 2023 . 10.1007/978-981-99-0835-6_35 Google Scholar Rawat , R. , Chakrawarti , R.K. , Raj , A. , Mani , G. , Chidambarathanu , K. , Bhardwaj , R. , Association rule learning for threat analysis using traffic analysis and packet filtering approach . Int. J. Inf. Technol. , 1 – 11 , 2023 . Google Scholar Rawat , R. , Logical concept mapping and social media analytics relating to cyber criminal activities for ontology creation . Int. J. Inf. Technol. , 15 , 2 , 893 – 903 , 2023 . Google Scholar Rawat , R. , Mahor , V. , Álvarez , J.D. , Ch , F. , Cognitive systems for dark web cyber delinquent association malignant data crawling: A review , in: Handbook of Research on War Policies, Strategies, and Cyber Wars , pp. 45 – 63 , 2023 . 10.4018/978-1-6684-6741-1.ch003 Google Scholar Rawat , R. , Chakrawarti , R.K. , Vyas , P. , Gonzáles , J.L.A. , Sikarwar , R. , Bhardwaj , R. , Intelligent fog computing surveillance system for crime and vulnerability identification and tracing . Int. J. Inf. Secur. Priv. (IJISP) , 17 , 1 , 1 – 25 , 2023 . 10.4018/IJISP.317371 Web of Science®Google Scholar R. Rawat , A.M. Sowjanya , S.I. Patel , V. Jaiswal , I. Khan , A. Balaram (Eds.), Using Machine Intelligence: Autonomous Vehicles Volume 1 , John Wiley & Sons , 2022 . Google Scholar Rawat , R. , Mahor , V. , Díaz-Álvarez , J. , Chávez , F. , Rooted learning model at fog computing analysis for crime incident surveillance , in: 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) , pp. 1 – 9 , IEEE , 2022 , December. 10.1109/SMARTGENCON56628.2022.10084316 Google Scholar Rawat , R. and Shrivastav , S.K. , SQL injection attack detection using SVM . Int. J. Comput. Appl. , 42 , 13 , 1 – 4 , 2012 . Google Scholar Rawat , R. , Bhardwaj , P. , Kaur , U. , Telang , S. , Chouhan , M. , Sankaran , K.S. , Smart vehicles for communication, volume 2 , John Wiley & Sons , 2023 . Google Scholar Mahor , V. , Garg , B. , Telang , S. , Pachlasiya , K. , Chouhan , M. , Rawat , R. , Cyber threat phylogeny assessment and vulnerabilities representation at thermal power station , in: Proceedings of International Conference on Network Security and Blockchain Technology: ICNSBT 2021 , pp. 28 – 39 , Springer Nature Singapore , Singapore , 2022 , June. 10.1007/978-981-19-3182-6_3 Google Scholar Rawat , R. , Gupta , S. , Sivaranjani , S. , Cu , O.K. , Kuliha , M. , Sankaran , K.S. , Malevolent information crawling mechanism for forming structured illegal organisations in hidden networks . Int. J. Cyber Warf. Terror. (IJCWT) , 12 , 1 , 1 – 14 , 2022 . 10.4018/IJCWT.311422 Web of Science®Google Scholar Conversational Artificial Intelligence ReferencesRelatedInformation